Analysis of Machine Learning Techniques to Identify and Mitigate Phishing Websites

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Abstract: This paper focuses on understanding and solving the problem of phishing websites. Phishing is a type of cyber attack that creates fake websites and URLs that look like real people and tricks people into sharing sensitive information, such as passwords or financial details. These attacks target unsuspecting users and can have serious consequences. In this study, we discuss how AI can help identify and prevent phishing websites. We also explore different AI models that could create smarter, more effective anti-phishing systems in the future. While phishing is quite a common problem, no single method can address all the vulnerabilities. Instead, an amalgamation of techniques is often needed to combat different types of attacks. Machine learning, in particular, is a powerful tool that can mitigate phishing attempts and ensure online safety for everyone. As technology advances, phishing approaches are becoming more sophisticated and often outpacing the potency of present antiphishing tools, which often have limitations. This paper explores how to use machine learning controls to combat phishing. The goal is to create an efficient, accurate, and effective system. To achieve this, we use four machine learning models: K- Nearest Neighbors (KNN), Kernel-SVM, Random Forest Classifier, and Decision Tree. These models rely on labeled datasets to learn and improve their ability to detect phishing attempts. Each of these algorithms brings unique strengths to the classification process, helping to identify and prevent malicious activity. Machine learning allows cybersecurity systems to analyze patterns, adapt to new threats, and take proactive steps to stop future attacks. By integrating machine learning into security, we can take important steps to protect users from these attacks